Bibliographic record
Abstract
Gout is a common hyperuricemic metabolic condition, leading to recurrent inflammatory arthritis and some of the most severe pain experienced by humans. As detailed in a recent Global Burden of Disease analysis of 195 countries and territories between 1990 and 2017,1 the incidence, prevalence, and disability burden of gout have risen worldwide for decades, and the condition now affects > 10 million US adults (4%).2 The disease burden of gout is also complicated by a higher prevalence of the metabolic syndrome and risk of cardiometabolic comorbidities.3 Further, this “modern gout epidemic” and suboptimal gout care4 have contributed to high rates of recurrent gout flares worldwide, rising ambulatory5 and emergency room visits,6 and hospitalizations due to gout over the past several decades.7,8 For example, from 1993 to 2011, US hospitalization rates due to gout doubled, whereas hospitalization rates for rheumatoid arthritis declined, narrowing the gap and soon reversing the rates between the 2 diseases.7 These data indicate the clear unmet need for improved gout prevention and care. Gout has historically been considered a disease of White men who overindulged in red meats and other rich foods, and epidemiologic studies have focused on White individuals. A few epidemiologic studies have reported higher risks of gout or hyperuricemia among Black individuals (Table 1),2,9,10,11 particularly among women9; however, no studies have evaluated the risks among other races or ethnicities in the US. Similarly, evaluation of gout risk factors (including genetics) has heavily focused on White individuals, including the deleterious factors of meat, seafood, and alcohol consumption, excess adiposity, and diuretic use,12 and protective factors of low-fat dairy and coffee consumption, vitamin C, and healthy dietary patterns.12,13 This stems from a … Address correspondence to Dr. H.K. Choi, Professor of Medicine, Harvard Medical School, Director, Gout and Crystal Arthropathy Center, Director, Clinical Epidemiology and Health Outcomes, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, 55 Fruit Street, Bulfinch 165, Boston, MA 02114, USA. Email: hchoi@mgh.harvard.edu.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".